
# resolution = "240p"
# aspect_ratio = "9:16"
num_frames = 17
fps = 24
frame_interval = 1
save_fps = 8
# save_dir = "/run/determined/NAS1/determined/sample/flow_progressive_delta/256_256_210000_1_128"
# save_dir = "samples/256_5_step"
save_dir = "/run/determined/NAS1/determined/sample/vbench/256_256_4_with_webvid_40000"
seed = 42
batch_siz4e = 1
multi_resolution = "STDiT2"
dtype = "bf16"
condition_frame_length = 5
align = 5

model = dict(
    type="STDiT3-XL/2",
    from_pretrained="/run/determined/NAS1/determined/checkpoints/with_webvid/checkpoint-40000/model",
    qk_norm=True,
    enable_flash_attn=True,
    enable_layernorm_kernel=False,
)
vae = dict(
    type="OpenSoraVAE_V1_2",
    from_pretrained="/run/determined/NAS1/OpenSora-VAE-v1.2/",
    micro_frame_size=17,
    micro_batch_size=4,
)
text_encoder = dict(
    type="t5",
    from_pretrained="/run/determined/NAS1/Open-Sora/T5/",
    model_max_length=300,
)
scheduler = dict(
    type="rflow",
    use_timestep_transform=True,
    num_sampling_steps=4,
    cfg_scale=2.0,
)

aes = 6.5
flow = 5
sample_method="no_cfg"
# prompt_path = "/run/determined/NAS1/determined/VBench/prompts/prompts_per_dimension/scene.txt"
# prompt_path = "./data/validate.txt"
prompt_path = "/home/yunzhu/opensora_distill/assets/texts/VBench/all_dimension.txt"
# prompt_path = "/home/yunzhu/opensora_distill/assets/texts/t2v_webvid.txt"
